I am a Python beginner.
At this moment, I am starting to develop and numerical algorithm, which is
supposed to do lot of calculations, but it doesn't use matrices at all, for
example.
So, at this moment, as I don't know much about NumPy and SciPy, and although
it is said that they are very useful and enhancers for developing scientific
Python applications, I don't see the point to use them...
For example, I don't know if there is a great advantage in using NumPy data
types (like bool_, int_ and so on) instead of the associated Python data
types (bool, int, etc.).
When I read NumPy and SciPy documentation, I found lots of new features that
I may use, but I haven't been able to find any quick explanation about how
those modules improve Python performance (something like "Instead of doing
*this* with standard Python, do *this* using NumPy and SciPy because it's
faster/better/whatever").
For example, what is the difference between "random" from random module and
"random" from numpy.random? Or are they the same?
Before I thought that working with NumPy+SciPy would be mandatory for me,
and so that I should have to adapt my code to all its special features, from
the beggining. But, at this moment, my strategy would be working with
"plain" Python, and when necessary, look for features I need in NumPy and
SciPy. Is it OK?
Can you give a light to me?
Sorry if I seem too rude or hard, I didn't mean to. I am just a bit lost...
Thank you in advance, and sorry for my English mistakes.
--
Vicent
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